Field of the Invention
This invention relates to a method and system for presenting seismic information sampled from geological formations.
History of the Related Art
Seismic studies represent an important means for mapping geological formations, for example for finding hydrocarbon resources or water reservoirs, by transmitting vibrations into the formations and detecting their reflections and refraction and in some cases transformations from pressure waves to shear waves.
These studies include large amounts of data using complex algorithms to provide a three dimensional map of the geological formations, where each point in the map is calculated based on the seismic data. After this process the operators interpret the map manually and based on their knowledge try to detect the promising geological structures possibly containing hydrocarbons or other resources.
Geophysical interpretation conventionally yields a single scenario for the configuration of subsurface geobodies such as faults and horizons, whereas the data generally support many possible interpretations. These interpretations are used for sophisticated analyses (Attribute Analysis) in which different properties of the data are explored, for example, the amplitude of seismic reflectors. Because conventional workflows only support a single model of many, attribute analysis results in only one instance of many. In general much work has been done in reducing the uncertainty of the data.
Several publications are known discussing the uncertainty of attribute data. Fournier et al (U.S. Pat. No. 5,638,269) describe a method for deducing geologic properties from seismic trace data. This method relies on obtaining calibration from local geologic data as measured in wells and seismic attributes obtained from data measured near the given wells. The relationship is then applied to the full seismic volume. Neelamani and Converse (US2010/0186950) describe a method for interpreting geologic features using attributes based on curvelet transforms of seismic data. While the authors discuss the calculation of multiple attributes based on a given curvelet transform of the data. Zhou and Ahmed (US2011/0213556) describe a method for seismic processing based on attributes. Unfortunately, it requires a waveform matching algorithm in order to reduce noise in the resulting seismic data. Neither of these methods relate to uncertainty of the attribute as such.
Fernandez (US2011/0307438) discloses a method for analyzing data in systems that have many variables (high-dimensional space). The method relies on reducing the dimensionality of the space to solve the problem more efficiently. Uncertainty as estimated in this disclosure represents the uncertainty in the lower/reduced dimensionality space, rather than the uncertainty associated with an attribute.
Rankin and Mitchel “Interpreter's Corner—That's why it's called interpretation: Impact of horizon uncertainty on seismic attribute analysis” The Leading Edge 22, 820 (2003) attempt to isolate the impact of interpretations on seismic attribute analysis. They perform a study in which six individual interpreters were asked to perform an interpretation of a single pinnacle reef structure in seismic data. An analysis of the impact of the variability in the interpretation results on seismic attributes as used to infer rock properties is performed. The authors instead suggest that their study implies that several interpretations could be used to generate end-member scenarios for risk assessment, and that these can be supplemented via subsequent geologic modeling. However, this study is deterministic and does not consider the probability of occurrence of a given attribute given a range of interpretations.
Several authors present methods for obtaining posterior distributions from simulations of subsurface models using rock physics relationships (for example, Sylta “Analysing exploration uncertainties by tight integration of seismic and hydrocarbon migration modeling” Petroleum Geoscience, August 2008, v. 14:219-221; Guillou et al “Hydrocarbonate reservoir characterization constrained to 3D seismic attributes” 2010; Mukerji et al: “Statistical rock physics: Combining rock physics, Information theory, and geostatistics to reduce uncertainty in seismic reservoir Characterization” MARCH 2001 THE LEADING EDGE 313.; Doyen and Den Boer: U.S. Pat. No. 5,539,704; Loures and Moraes: “Petrophysical Reservoir Characterization and Uncertainty Analysis”, 2003). In these cases, there is an inherent and acknowledged uncertainty associated with relating the seismic data (amplitude and phase) to lithology and fluid content based on the physical response of the rocks. The authors use different techniques to explore this uncertainty, combining forward modeling (simulation) and known relationships to obtain distributions of predicted lithology parameters. Unfortunately, these methods require sophisticated forward models, detailed lithology information from existing wells, and most importantly, ignore uncertainty in the data and the physical response. They are therefore unsuited towards producing ensembles of geophysical attributes for statistical analysis.
Wellmann, F. J. et al: “Towards incorporating uncertainty of structural data in 3D geological inversion” Tectonophysics (2010), N. 124902, discloses a method for generating multiple realizations of subsurface structure via simulation and optimization. As discussed in section 1.1, these realizations are described as mathematical functions, which are constructed via locations and attidutes (e.g., dip) of measured data. This set of multiple realizations of subsurface structure (faults, horizons, etc) are considered as an input to the method according to the present invention. The geobody ensemble (consisting of multiple realizations of a geobody) is input to our invention. While Wellmann provides a method for obtaining said geobody ensemble, it does not analyze the ensemble via an attribute function, and does not produce an ensemble of attribute realizations that can be statistically analyzed.
US 2005007876 discusses a method for determining fluid properties via inversion of seismic attribute data. The method may use multiple attribute maps; however, these attribute maps are for a single configuration of a horizon of interest (see 0018, FIG. 1). It is common practice in the industry that many different types of attributes be calculated from seismic data, and that is not the subject of our invention. It does not consider the use of an ensemble of horizons for each particular attribute map, nor does it generate an ensemble of attribute maps based on an input ensemble of horizon configurations.